Abstract
Despite the significant progress made in scheduling in the past years, industrial problems with several hundred tasks remain intractable for some variants of the scheduling problems. We present techniques that can be used to leverage the power of constraint programming to solve an industrial problem with 800 non-preemptive tasks, 90 resources, and sequence-dependent setup times. Our method involves solving the traveling salesperson problem (TSP) as a simplification of the scheduling problem and using the simplified solution to guide the branching heuristics. We also explore large neighborhood search. Experiments conducted on a dataset provided by our partner from the textile industry show that we obtain non-optimal but satisfactory solutions.
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Notes
- 1.
The MiniZinc files are freely available on Claude-Guy Quimper’s web site or directly at http://www2.ift.ulaval.ca/~quimper/publications/CPAIOR2020Submission.zip.
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Mercier-Aubin, A., Gaudreault, J., Quimper, CG. (2020). Leveraging Constraint Scheduling: A Case Study to the Textile Industry. In: Hebrard, E., Musliu, N. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2020. Lecture Notes in Computer Science(), vol 12296. Springer, Cham. https://doi.org/10.1007/978-3-030-58942-4_22
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